SOTAVerified

Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

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Showing 8001–8025 of 17610 papers

TitleStatusHype
OpenECAD: An Efficient Visual Language Model for Editable 3D-CAD Design—0
Rapport-Driven Virtual Agent: Rapport Building Dialogue Strategy for Improving User Experience at First MeetingCode0
PARSE-Ego4D: Personal Action Recommendation Suggestions for Egocentric Videos—0
ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy ModelsCode0
Talking Heads: Understanding Inter-layer Communication in Transformer Language Models—0
RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQL—0
Transformers meet Neural Algorithmic Reasoners—0
Autonomous Multi-Objective Optimization Using Large Language Model—0
Multi-Modal Retrieval For Large Language Model Based Speech Recognition—0
On the Effects of Heterogeneous Data Sources on Speech-to-Text Foundation Models—0
LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language ModelsCode0
Zero-Shot Learning Over Large Output Spaces : Utilizing Indirect Knowledge Extraction from Large Language Models—0
Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning—0
Decoding the Diversity: A Review of the Indic AI Research Landscape—0
CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracer—0
Investigating the translation capabilities of Large Language Models trained on parallel data onlyCode0
ElicitationGPT: Text Elicitation Mechanisms via Language Models—0
An Approach to Build Zero-Shot Slot-Filling System for Industry-Grade Conversational Assistants—0
DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding—0
Generative AI-based Prompt Evolution Engineering Design Optimization With Vision-Language Model—0
DubWise: Video-Guided Speech Duration Control in Multimodal LLM-based Text-to-Speech for Dubbing—0
Chain-of-Though (CoT) prompting strategies for medical error detection and correction—0
Conceptual Learning via Embedding Approximations for Reinforcing Interpretability and TransparencyCode0
Guiding In-Context Learning of LLMs through Quality Estimation for Machine TranslationCode0
Analyzing constrained LLM through PDFA-learningCode0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified